Managing semantic data in a contextual workspace

ABSTRACT

Techniques for managing a virtual workspace include: generating a virtual workspace viewable by a user on a graphical user interface, the virtual workspace comprising a plurality of workspace modules comprising data contained in a plurality of data objects; identifying an interaction by the user with at least some of the data contained in a particular data object of the plurality of data objects; and providing, through the virtual workspace, at least one suggestion comprising a description of data contained in the plurality of data objects that is semantically related to the data interacted with by the user.

TECHNICAL BACKGROUND

This disclosure relates to using a contextual workspace to view, modify,and/or manage business data for a business enterprise.

BACKGROUND

Business users of software in a business enterprise may utilize avirtual workspace to browse, view, modify, and/or otherwise manipulatedata related to the business enterprise. Such data may include a varietyof information in many different forms, such as sales data, revenuedata, human resources information, business hierarchy information, andotherwise. Graphs, tables, charts, electronic communications, webservices, reports, and other forms of data, may be viewable in theuser's workspace. The workspace may allow or facilitate the resolutionof business issues and/or problems by the user. In some situations,however, the workspace itself has no semantic knowledge of the contentbeing viewed and/or manipulated by the user. Thus, the workspace may notable to provide services to the user that rely on a semantic context ofdata (active or passive) in the workspace, i.e., one or more workspacemodules that are on viewable and/or modifiable on the workspace at agiven moment. Contextual information, however, may enable the user toidentify relationships between existing modules active in the workspace,relationships between active modules and additional data related to thebusiness enterprise, and, generally, enrich the workspace context.

In some instances, there may be relevant data that is unknown to thebusiness user, such as, for example, additional applications, reports,data feeds, or simply raw data from different content providers. Forinstance, to react to some business event or to proceed with a businessprocess, the user may need to add relevant content to the workspace tohave additional insights or perform further actions. Finding therelevant content is a complex task as each content source offers a lotof content in different formats and protocols, which are not necessarilyunderstood or exposed to the user. Moreover, searching each contentprovider or other sources of business data for the relevant content isnot trivial, as search terms are often complex and content providersmight not offer usable search mechanisms.

In some cases, relevant content that may assist the business user in abusiness event or business process may include content previouslygenerated, viewed, and/or used by a business colleague (e.g., anotherbusiness user in the same business unit, a different business unit,and/or with a similar role in the business enterprise). The businessuser, however, may have no way to leverage this internal knowledge aboutwhat other colleagues did in similar business situations.

Should such relevant content become available to the business userthrough the workspace, it can often be disjointed and unmanageable. Forinstance, certain relevant information may be more critical to thebusiness user compared to other relevant information. For instance,relevant information that a colleague with a similar business role foundto be important may be more critical than other information. Thus, asdata is collected from various content providers, it may be culledand/or prioritized according to different criteria.

SUMMARY

The present disclosure describes one or more general embodiments formanaging a virtual workspace including: generating a virtual workspaceviewable by a user on a graphical user interface, the virtual workspacecomprising a plurality of workspace modules comprising data contained ina plurality of data objects; identifying an interaction by the user withat least some of the data contained in a particular data object of theplurality of data objects; and providing, through the virtual workspace,at least one suggestion comprising a description of data contained inthe plurality of data objects that is semantically related to the datainteracted with by the user.

A first aspect combinable with any of the general embodiments mayinclude receiving the suggestion from a content provider communicablycoupled to the virtual workspace.

In a second aspect combinable with any of the general embodiments,identifying an interaction by the user with at least some of the datacontained in the plurality of data objects comprises at least one of:identifying a workspace module added to the virtual workspace by theuser; identifying a selection of a workspace module in the plurality ofworkspace modules; identifying a selection of the data contained in theparticular data object; or identifying a metric of the particular dataobject among a plurality of metrics.

A third aspect combinable with any of the general embodiments mayinclude identifying metadata associated with the data interacted with bythe user.

A fourth aspect combinable with any of the general embodiments mayinclude identifying the data contained in the plurality of data objectsthat is semantically related to the data interacted with by the userbased on the identified metadata.

A fifth aspect combinable with any of the general embodiments mayinclude generating a plurality of suggestions, each suggestioncomprising a description of data contained in the plurality of dataobjects that is semantically related to the data interacted with by theuser.

A first aspect combinable with any of the general embodiments mayinclude ranking the plurality of identified suggestions based on acalculated relevancy of each of the plurality of identified suggestions.

In a sixth aspect combinable with any of the general embodiments,ranking the plurality of identified suggestions based on a calculatedrelevancy of each of the plurality of identified suggestions includesdetermining, for each data object, an occurrence frequency of at leastone keyword from the metadata associated with the data interacted withby the user in the data contained in the plurality of data objects; anddetermining an order of the data objects based on the determinedoccurrence frequencies, wherein a data object having the highestoccurrence frequency is at a top of the order and a data object having alowest occurrence frequency is at a bottom of the order.

In a seventh aspect combinable with any of the general embodiments, theplurality of data objects include a first data object and a second dataobject.

An eighth aspect combinable with any of the general embodiments mayinclude identifying at least one metric of the first data object.

A ninth aspect combinable with any of the general embodiments mayinclude matching the identified metric to a metric of the second dataobject.

A tenth aspect combinable with any of the general embodiments mayinclude generating the suggestion comprising a description of datacontained in the second data object.

In an eleventh aspect combinable with any of the general embodiments,the plurality of data objects may include a plurality of data cubes.

In a twelfth aspect combinable with any of the general embodiments,identifying an interaction by the user with at least some of the datacontained in a particular data object of the plurality of data objectsmay include identifying an interaction by the user with at least some ofthe data contained in a particular data cube of the plurality of datacubes.

A thirteenth aspect combinable with any of the general embodiments mayinclude identifying at least one metric of the particular data cubeinteracted with by the user.

A fourteenth aspect combinable with any of the general embodiments mayinclude searching the plurality of data cubes for another data cubehaving the identified metric of the particular data cube interacted withby the user.

A fifteenth aspect combinable with any of the general embodiments mayinclude generating the suggestion comprising a description of datacontained in the other data cube.

A sixteenth aspect combinable with any of the general embodiments mayinclude querying the other data cube for at least one view of datacontained in the other data cube that matches a view of data containedin at least one workspace module in the virtual workspace viewable inthe graphical user interface.

A seventeenth aspect combinable with any of the general embodiments mayinclude generating the suggestion comprising a description of the viewof data contained in the other data cube.

An eighteenth aspect combinable with any of the general embodiments mayinclude searching the plurality of data cubes for at least one reportutilizing the view of data contained in the other data cube that matchesa view of data contained in at least one workspace module.

A nineteenth aspect combinable with any of the general embodiments mayinclude generating the suggestion comprising a description of the reportutilizing the view of data contained in the other data cube.

Various embodiments of a contextual virtual workspace according to thepresent disclosure may have one or more of the following features. Forexample, the contextual workspace may add data content sources (e.g.,workspace modules containing data from the content sources) to theworkspace, thereby enabling a business user of the workspace to consumedata from various sources. The contextual workspace may also apply therelevant workspace module on the workspace at runtime. Further, thecontextual workspace may also provide for and/or add a semantic contextto the workspace. The semantic context may be aware of all the moduleson the workspace (e.g., which module was lately interacted with, whichmodule is active, and otherwise) and enable collection of semanticinformation on each module (e.g., type, title, and content attributes aswell as what operations were done by the user on each module). Thecontextual virtual workspace may be, therefore, ‘smart” in that it mayexpose semantic content and enable contextual services (e.g.,suggestions) based on the content. The contextual workspace may alsoidentify context by combining known information on the contents of theworkspace and the user interaction with this content to create ameaningful semantic context. The contextual workspace may thus focus onusing content and/or user-related metadata (e.g., collected explicitlyor implicitly in the user working environment) to find other relevantcontent. For example, the contextual workspace may search for content inconcrete data and/or data relationships in different content providers(e.g., searching for a metric extracted from a report used by the userin his workspace and looking for data, such as OLAP data cubes orreports, that use the same metric in the some content provider). Asanother example, the contextual workspace may suggest workspace modulesto add to the workspace according to, for instance, a user's lastaction, one or more combinations of the workspace similarity and otherusers behavior patterns, user similarity with other users, timeproximity, and other factors. Thus, the contextual workspace may utilizesocial connections (e.g., role of a user in an enterprise, position ofthe user, organization unit of the user) to formulate a weightcoefficient to apply to more data and/or suggestions

Various embodiments of a contextual virtual workspace according to thepresent disclosure may also include one or more of the followingfeatures. For example, the contextual workspace may reflect and/orprovide business data in real-time through one or more workspace modulesthat may be updated dynamically. In the present disclosure, the term“real-time” refers to a substantially instantaneous or nearinstantaneous event. In other words, “real-time” may refer to theconcept of user-interaction time, i.e., the amount of processing time byor with the contextual virtual workspace is less than the user'sreaction time (e.g., less than 0.5 seconds). The contextual workspacemay also search and/or manipulate well modeled tables and cubes forsemantic services (e.g., suggestions, simplified data acquisition);semantic services in our solutions occurs in time of user interaction(real time). For example, the contextual workspace may include and/or becoupled with a highly parallelized computational engine withcomputationally intensive UI patterns (e.g., real time alerts,suggestions). In some aspects, the contextual virtual workspace may becommunicably coupled with an in-memory database that allows the user,through the workspace, to instantly (or near instantly) explore andanalyze transactional and analytical data from virtually any data sourcein real-time. Operational data is captured in memory as businesshappens, and flexible views expose analytic information at the speed ofthought. External data can be added to analytic models to expandanalysis across an entire business enterprise. In other words, businesslogic may be closely coupled next to data.

Various embodiments of a contextual virtual workspace according to thepresent disclosure may also include one or more of the followingfeatures. For example, the contextual workspace may collect informationabout one or more active workspace modules and then recognize relevantcontent data according to, e.g., metadata, user-interface (UI orworkspace module) content, such as headers of a table, activeness of themodule, and other metrics. Thus the workspace may push suggestions tothe user based on the recent tasks, such as a recent action taken thatchanges the content of his/her workspace. As another example, thecontextual workspace may identify relevant content as the basis ofsuggestions in the form of a title of a module (e.g., a snapshot ofbusiness explorer description); metadata (e.g., names of columns ordescriptions of columns of a table); a cube (e.g., measures and/ordimensions of an OLAP cube or other data structure allowing fast accessof arrayed data, facet-filtering the cube by measure and/or dimension);reports (e.g., prompts/input fields); location of user's input device inworkspace; and/or user selection of text in a particular workspacemodule.

These general and specific aspects may be implemented using a device,system or method, or any combinations of devices, systems, or methods.The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example distributed computing system operable togenerate, view, modify, and/or otherwise manipulate a contextualworkspace according to the present disclosure;

FIGS. 2A-2B illustrate additional example distributed computing systemsoperable to generate, view, modify, and/or otherwise manipulate acontextual workspace according to the present disclosure;

FIG. 3 illustrates an example virtual distributed computing systemoperable to generate, view, modify, and/or otherwise manipulate acontextual workspace according to the present disclosure;

FIGS. 4A-4C illustrate example contextual workspaces having one or moreworkspace modules according to the present disclosure;

FIG. 5 illustrates an example method for using a contextual workspaceaccording to the present disclosure;

FIG. 6 illustrates another example method for using a contextualworkspace according to the present disclosure;

FIG. 7 illustrates another example method for using a contextualworkspace according to the present disclosure;

FIGS. 8A-8C graphically illustrate an example algorithm implemented by acontextual workspace according to the present disclosure; and

FIG. 9 illustrates another example method for using a contextualworkspace according to the present disclosure.

DETAILED DESCRIPTION

FIG. 1 illustrates an example distributed computing system 100 operableto generate, view, modify, and/or otherwise manipulate a contextualworkspace. According to the present disclosure, a contextual workspacemay be a virtual workspace viewable (e.g., through a graphical userinterface of a particular computing device), modifiable, and orengageable by a user that presents business data connected or relatedaccording to one or a number of business contexts. As explained morefully below, the business data may be presented to the user in any of avariety of techniques and views, such as, for example, reports, tables,notes, graphs, and other views. The business data may be connectedand/or related according to any of a variety of contexts, such as, forexample, a particular business issue or problem, a particular product orservice, a particular organizational unit and/or portion of a businessenterprise, across a particular position and/or user role found withinthe business enterprise, and other contexts. Thus, the contextualworkspace may help the user solve or attack a business problem, manageone or more products and/or services offered by the business enterprise,and/or take advantage of existing organizational and/or userrelationships established across the enterprise.

In some embodiments, a “user role” may be a role of a user withinhis/her business enterprise as determined in the enterprise's corporatehuman resource (HR) system(s) and its affinity to groups anddepartments, and a role that may be a dynamically calculated businessnetwork relationship within the enterprise based on business solutionsdeveloped through one or more enterprise applications. It may include,but not be limited to, participation in particular projects, havingparticular expertise, participation in the same (e.g., ad hoc) workinggroups, and otherwise.

The illustrated computing environment 100 includes a server system 105,a client system 110, and a remote computing system 130 communicablycoupled through a network 120. Although illustrated as single systems,each of the systems 105, 110, and 130 may include more than one systemand/or more than one computing device (e.g., computer, laptop, server,mobile device, and otherwise) within a distributed computingenvironment. In general, computing environment 100 depicts an exampleconfiguration of a system capable of providing stateful execution ofstateless applications in a substantially transparent way, as well asdynamically determining the particular application's mode of operationin response to requests from its clients (e.g., client appliances 125).

The illustrated server system 105 includes one or more server appliances115 having corresponding graphical user interfaces (GUIs) 117. Ingeneral, the server appliance 115 is a server that stores one or moreapplications, where at least a portion of the applications are executedvia requests and responses sent to users or clients within andcommunicably coupled to the illustrated environment 100 of FIG. 1. Insome instances, the server appliance 115 may store a plurality ofvarious hosted applications, while in other instances, the serverappliance 115 may be a dedicated server meant to store and execute onlya single hosted application (e.g., the contextual workspace (server)160). In some instances, the server appliance 115 may comprise a webserver, where the applications, such as the contextual workspace(server) 160 represent one or more web-based applications accessed andexecuted via network 120 by the client appliances 125 of the environment100 to perform the programmed tasks or operations of the hostedapplications.

At a high level, the server appliance 115 comprises an electroniccomputing device operable to receive, transmit, process, store, ormanage data and information associated with the environment 100.Specifically, the server appliance 115 illustrated in FIG. 1 isresponsible for receiving application requests from one or moreapplications (e.g., associated with the clients 125 of environment 100and responding to the received requests by processing said requests inthe associated hosted application 114, and sending the appropriateresponse from the hosted application 114 back to the requesting clientapplication 144. In addition to requests from the external clients 125illustrated in FIG. 1, requests associated with the hosted applications114 may also be sent from internal users, external or third-partycustomers, other automated applications, as well as any otherappropriate entities, individuals, systems, or computers.

As used in the present disclosure, the term “computer” is intended toencompass any suitable processing device. For example, although FIG. 1illustrates a single server appliance 115, environment 100 can beimplemented using two or more server appliances 115, as well ascomputers other than servers, including a server pool. Indeed, serverappliance 115 may be any computer or processing device such as, forexample, a blade server, general-purpose personal computer (PC),Macintosh, workstation, UNIX-based workstation, or any other suitabledevice (e.g., smartphones, PDAs, tablets). In other words, the presentdisclosure contemplates computers other than general purpose computers,as well as computers without conventional operating systems. Further,illustrated server appliance 115 may be adapted to execute any operatingsystem, including Linux, UNIX, Windows, Mac OS, or any other suitableoperating system.

The illustrated server appliance 115 is communicably coupled within-memory database 140 in the server system 105. In some embodiments,server 115 and/or certain of its components may be integrated withdatabase 140 so that, for instance, processing (e.g., all or partial)may be performed directly on in-memory data with processing resultspassed (e.g., via a communication channel) directly to a client. Inalternative embodiments, the in-memory database 140 may be locatedexternal to the server system 105 and communicably coupled to one ormore of the server system 105 and/or client system 110 through thenetwork 120. The illustrated in-memory database 140 may includeintegrated processing, i.e., all business and/or analytic operationsdone in processing memory. Moreover, content from business contentsources (described more fully below) may be replicated from one or moretransactional systems (e.g., coupled to the network 120) to thein-memory database 140 immediately. Thus, the in-memory database 140, insome aspects, may handle the analytical systems for all business data inreal-time, as opposed to, for instance, computational processing systemsthat have separate transactional and analytical systems that connectthrough relational databases (i.e., relational databases stored onmagnetic memory that require a process, e.g., ETL, to transfer data fromone system to another not in real time but with a delay of an hour, day,week, or longer).

In some embodiments, the in-memory database 140 may expose business dataand capabilities to improve an end-solution for end users (e.g., theclient appliances 125). The in-memory database 140 may reside on top ofa computational engine (e.g., in the server appliance 115 or otherwise)that facilitates fast manipulations on large amounts of business dataand/or replication of entire business suite information. Thus, in someembodiments, the in-memory database may provide for the following designprinciples/concepts: business data in real-time (e.g., GUI patterns forconstantly updated business data); well modeled tables and data cubes(e.g., in order to provide semantic services); a highly parallelizedcomputational engine (e.g., for computationally intensive GUI patternssuch as real time alerts and/or suggestions); close coupling of businesslogic and business data (e.g., eliminating indexing and caching).

The illustrated in-memory database 140 stores one or more data objects143. The data objects 143 may include and/or reference a variety ofobjects that store and/or include business data. For instance, the dataobjects 143 may be data cubes, such as OLAP (online analyticalprocessing) cubes. The data cubes may consist of a data structure thatallows for columnar data storage rather than, e.g., row data storage;different types of indices compared to relational databases; andin-memory technology as compared to data stored in relational databases.The data cube may also allow manipulation and/or analysis of the datastored in the cube from multiple perspectives, e.g., by dimensions,measures, and/or elements of the cube. A cube dimension defines acategory of data stored in the cube, for example, a time duration ofcertain business data, a product or service, business user roles, and avariety of other categories. In other words, a cube dimension may be oneway to slice business data stored in the cube according to some businesslogic (e.g., logic within and/or associated with the contextualworkspace modules). In some instances, the data cube may havethree-dimensions, but any number of dimensions may be designed into thecube (e.g., a hypercube).

A cube measure may be a fact, such as a numeric fact, that iscategorized into one or more dimensions. Measures may include, forexample, specific product sales data according to a set period of time.Measures may also include, for example, manufacturing efficiency datafor a particular organizational unit of a business enterprise. In short,measures may include any appropriate business data that may bemanipulated according to business logic to assist or support thebusiness enterprise.

One or more functions may be performed on a data cube. For instance, thedata cube may be pivoted, in various ways. Each pivot provides thebusiness user with a distinct view of particular business data stored inthe cube. For instance, in one view, a business user may be presentedwith sales data of a specific data within a particular geographic regionacross a particular time period with a particular focus on the sales vs.geography relationship. In another view, the same data (e.g., the samemeasures and elements) may be presented with a different focus, e.g.,the sales vs. time period relationship. In some aspects, pivoting a datacube in real-time may allow the business user to more efficientlyanalyze the business data.

Other functions performable on data cubes may be, for instance, slice,dice, drill down/up, and roll-up. A slice operation identifies a subsetof a multi-dimensional array corresponding to a single value for one ormore members of the cube dimensions not in the subset. A dice operationis a slice operation on more than two dimensions of a data cube (or morethan two consecutive slices). A drill down/up operation allows thebusiness user to navigate the data cube's levels of data to reveallevels containing the most summarized (up) data to the most detailed(down) data. A roll-up operation involves computing all of the datarelationships for one or more dimensions of the data cube.

The data objects 143 may include and/or store other forms of data alongwith or in place of data cubes. For example, the data objects 143 mayrepresent, store, and/or reference data from one or more contentsources, such as web content, feeds, REST services, business datarepositories, reports, status updates, discussions, wikis, blogs, andother content sources. Of course, while illustrated as contained in thein-memory database 140, the data objects 143 may also be stored, forexample, in one or both of the memories 155, in the remote computingsystem 130, and/or a separate repository communicably coupled to thenetwork 120. In some embodiments, the data objects 143 may be stored ina raw, compiled, or compressed form or combination thereof.

The illustrated client system 110 includes one or more client appliances125 having corresponding GUIs 127. Each client appliance 125 may be anycomputing device operable to connect to or communicate with at least theserver system 105 and/or via the network 120 using a wireline orwireless connection. Further, as illustrated, each client appliance 125includes a processor 150, an interface 145, and a memory 155. Ingeneral, each client appliance 125 comprises an electronic computerdevice operable to receive, transmit, process, and store any appropriatedata associated with the environment 100 of FIG. 1. It will beunderstood that there may be any number of client appliances 125associated with, or external to, environment 100. For example, whileillustrated environment 100 illustrates three client appliances,alternative implementations of environment 100 may include a singleclient appliance 125 communicably coupled to the server system 105, orany other number suitable to the purposes of the environment 100.

Additionally, there may also be one or more additional client appliances125 external to the illustrated portion of environment 100 that arecapable of interacting with the environment 100 via the network 120.Further, the term “client” and “user” may be used interchangeably asappropriate without departing from the scope of this disclosure.Moreover, while each client appliance 125 is described in terms of beingused by a single user, this disclosure contemplates that many users mayuse one computer, or that one user may use multiple computers. As usedin this disclosure, client appliance 125 is intended to encompass atablet computing device, personal computer, touch screen terminal,workstation, network computer, kiosk, wireless data port, smart phone,personal data assistant (PDA), one or more processors within these orother devices, or any other suitable processing device. For example,each client appliance 125 may comprise a computer that includes an inputdevice, such as a keypad, touch screen, mouse, or other device that canaccept user information, and an output device that conveys informationassociated with the operation of the server system 105 or the clientappliance 125 itself, including digital data, visual information, anyapplication, or the GUI 127. Both the input and output device mayinclude fixed or removable storage media such as a magnetic storagemedia, CD-ROM, or other suitable media to both receive input from andprovide output to users of the client appliances 125 through thedisplay, namely, the GUI 127.

The illustrated network 120 facilitates wireless or wirelinecommunications between the components of the environment 100 (i.e.,between the server system 105 and the client system 110), as well aswith any other local or remote computer (e.g., remote computing system130), such as additional clients, servers, or other devices communicablycoupled to network 120 but not illustrated in FIG. 1. The network 120 isillustrated as a single network in FIG. 1, but may be a continuous ordiscontinuous network without departing from the scope of thisdisclosure, so long as at least a portion of the network 120 mayfacilitate communications between senders and recipients. The network120 may be all or a portion of an enterprise or secured network, whilein another instance at least a portion of the network 120 may representa connection to the Internet. In some instances, a portion of thenetwork 120 may be a virtual private network (VPN), such as, forexample, the connection between the client system 110 and the serversystem 105.

Further, all or a portion of the network 120 can comprise either awireline or wireless link. Example wireless links may include802.11a/b/g/n, 802.20, WiMax, and/or any other appropriate wirelesslink. In other words, the network 120 encompasses any internal orexternal network, networks, sub-network, or combination thereof operableto facilitate communications between various computing components insideand outside the illustrated environment 100. The network 120 maycommunicate, for example, Internet Protocol (IP) packets, Frame Relayframes, Asynchronous Transfer Mode (ATM) cells, voice, video, data, andother suitable information between network addresses. The network 120may also include one or more local area networks (LANs), radio accessnetworks (RANs), metropolitan area networks (MANs), wide area networks(WANs), cellular networks, all or a portion of the Internet, and/or anyother communication system or systems at one or more locations.

The illustrated remote computing system 130 is communicably coupled toone or both of the server system 105 and client system 110 through thenetwork 120. In some instances, as illustrated, the remote computingsystem 130 stores and/or references third party content 135, such as,for example, data objects, web content, electronic communications,content feeds, and other data sources. Although illustrated as a singleappliance, the remote computing system 130 may include any number ofappliances (e.g., servers, clients, mobile devices, and otherwise)coupled to the network 120 individually and/or in groups. For instance,in some embodiments, the remote computing system 130 may be a webcontent server delivering web content to one or more of the clientappliances 125 in response to a request. In some embodiments, the remotecomputing system 130 may be a repository storing one or more dataobjects, such as data cubes or other form of database storing businessdata.

The illustrated communication interfaces 145 (shown as part of theserver appliance 115 and the client appliance 125) facilitatecommunication among appliances in, for example, the client system 110,the remote computing system 130, and the server system 105. Theinterfaces 145 may also facilitate communication among the illustratedsystems and other systems in a client-server or other distributedenvironment (including within environment 100) connected to the network.Generally, the interfaces 145 include logic encoded in software and/orhardware in a suitable combination and operable to communicate with thenetwork 120. More specifically, the interfaces 145 may include softwaresupporting one or more communication protocols associated withcommunications such that the network 120 or interface's hardware isoperable to communicate physical signals within and outside of theillustrated environment 100.

The illustrated processors 150 (shown as part of the server appliance115 and the client appliance 125) may be a central processing unit(CPU), a blade, an application specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or another suitable component.Generally, the processors 150 execute instructions and manipulate datato perform the operations of the respective server appliance 115 andclient appliance 125 and, specifically, the contextual workspace module(server) 160 and contextual workspace module (client) 165), as well asany other applications. Specifically, the server appliance's processor150 executes the functionality required to receive and respond torequests from the client appliances 125 and their respectiveapplications (e.g., contextual workspace (client) 165), as well as thefunctionality required to perform the other operations of the contextualworkspace module (server) 160.

Regardless of the particular implementation, “software” may includecomputer-readable instructions, firmware, wired or programmed hardware,or any combination thereof on a tangible medium operable when executedto perform at least the processes and operations described herein.Indeed, each software component may be fully or partially written ordescribed in any appropriate computer language including C, C++, Java,Visual Basic, assembler, Perl, any suitable version of 4GL, as well asothers. It will be understood that while portions of the softwareillustrated in FIG. 1 are shown as individual modules that implement thevarious features and functionality through various objects, methods, orother processes, the software may instead include a number ofsub-modules, third party services, components, libraries, and such, asappropriate. Conversely, the features and functionality of variouscomponents can be combined into single components as appropriate.Although illustrated as a single processor 150 for each of therespective server appliance 115 and client appliance 125 in FIG. 1, twoor more processors may be used according to particular needs, desires,or particular embodiments of environment 100.

The illustrated memories 155 (shown as part of the server appliance 115and the client appliance 125) may include any memory or database moduleand may take the form of volatile or non-volatile memory including,without limitation, magnetic media, optical media, random access memory(RAM), read-only memory (ROM), removable media, or any other suitablelocal or remote memory component. Each memory 155 may store variousobjects or data, including classes, frameworks, applications, backupdata, business objects, jobs, web pages, web page templates, databasetables, repositories storing business and/or dynamic information, andany other appropriate information including any parameters, variables,algorithms, instructions, rules, constraints, or references theretoassociated with the purposes of the respective server appliance 115 andclient appliance 125. Additionally, each memory 155 may include anyother appropriate data, such as VPN applications, firmware logs andpolicies, firewall policies, a security or access log, print or otherreporting files, as well as others.

As illustrated, memory 155 of the server appliance 115 includes and/orstores one or more server content objects 170. Memory 155 of the clientappliance 125 includes and/or stores one or more client content objects175. In some embodiments, the server content objects 170 and/or theclient content objects 175 may be similar to the data objects 143 storedin the in-memory database 140. For example, the content objects 170 and175 may be data cubes, tables, reports, or other content sources, suchas web content, electronic communications, feeds, and otherwise.Regardless of the form of the content objects 170 and 175, these objectsmay contain and/or reference business data on which business logic maybe applied, e.g., by the contextual workspace module (server) 160 and/orcontextual workspace module (client) 165, in order to realize and/oraccomplish a task in a business environment.

The illustrated computing system 100 includes a contextual workspacemodule (server) 160 and a contextual workspace module (client) 165. At ahigh level, each of the contextual workspace module (server) 160 and acontextual workspace module (client) 165 (referred to collectively asthe contextual workspace modules) is any application, program, module,process, or other software that may execute, change, delete, generate,or otherwise manage information according to the present disclosure,particularly in response to and in connection with one or more requestsreceived from the illustrated client appliances 125 and their associatedapplications. In certain cases, only one contextual workspace module(server) 160 may be located at a particular server appliance 115. Inothers, a plurality of related and/or unrelated contextual workspacemodule (server) 160 may be stored at a single server appliance 115, orlocated across a plurality of other server appliances 115 in the serversystem 105, as well.

In certain cases, the contextual workspace modules may be implemented ascomposite applications. For example, portions of the compositeapplication may be implemented as Enterprise Java Beans (EJBs) ordesign-time components may have the ability to generate runtimeimplementations into different platforms, such as J2EE (Java 2 Platform,Enterprise Edition), ABAP (Advanced Business Application Programming)objects, or Microsoft's .NET, among others. Additionally, the contextualworkspace modules may represent web-based applications accessed andexecuted by remote client appliances 125 or client applications via thenetwork 120 (e.g., through the Internet).

Further, while illustrated as internal to server appliance 115, one ormore processes associated with the contextual workspace module (server)160 may be stored, referenced, or executed remotely. For example, aportion of the contextual workspace module (server) 160 may be a webservice associated with the application that is remotely called, whileanother portion of the contextual workspace module (server) 160 may bean interface object or agent bundled for processing at a remote clientappliance 125 via the contextual workspace module (client) 165.Moreover, any or all of the contextual workspace modules may be a childor sub-module of another software module or enterprise application (notillustrated) without departing from the scope of this disclosure.

As explained more fully below, one or both of the contextual workspacemodules 160 and 165 may present or facilitate presentation of a userinterface, e.g., through GUI 117 and/or GUI 127, to a business user toperform a variety of tasks. For instance, the contextual workspacemodules 160 and 165 may present one or more workspace modules containingbusiness data relevant to the business user in a variety of forms, e.g.,tables, graphs, notes, and otherwise. The relevant business data may besourced from, for example, the data objects 143 or other contentsources.

FIGS. 2A-2B illustrate additional example distributed computing systems200 and 250, respectively, operable to generate, view, modify, and/orotherwise manipulate a contextual workspace. Distributed computingsystems 200 and 250, in some embodiments, may illustrate two differentexample configurations for deployment of a contextual workspace within adistributed computing environment. For example, in some instances,either of the distributed computing systems 200 or 250 may represent analternate deployment of a contextual workspace within a distributedcomputing environment to the computing system 100 shown in FIG. 1.

Turning to FIG. 2A, the illustrated distributed computing system 200includes a web portal 205 communicably coupled to a virtual appliance215 and an in memory database 220. In some embodiments, the web portal205 provides an authentication portal allowing a single point of accessto applications, business data, and services both within and external toa business enterprise. For instance, an employee or user of the businessenterprise may access the virtual appliance 215 and/or the in-memorydatabase 220 via the web portal 205. In other words, the web portal 205may provide a front end accessible through a client appliance located atthe business enterprise.

The illustrated web portal 205 includes a client enterprise workspace210. In some aspects, the client enterprise workspace 210 may besubstantially similar to the contextual workspace module (client) 165shown in FIG. 1. For instance, the client enterprise workspace 210 maypresent or facilitate presentation of a user interface (e.g., one ormore workspace modules described more fully below) to the business userto perform a variety of tasks.

The illustrated virtual appliance 215 may be substantially similar tothe server appliance 115 illustrated in FIG. 1 and include a serverenterprise workspace 216, a business intelligence module 217, and anopen source module 218. The server enterprise workspace 216, in someimplementations, may be substantially similar to the contextualworkspace module (server) 160 shown in FIG. 1. For instance, the serverenterprise workspace 216 may provide for a customizable and dynamicvirtual workspace accessible to a client and/or user via a GUI, e.g.,through the web portal 205. In some aspects, as explained more fullybelow, such a virtual contextual workspace may provide for a number offunctional features and advantages, such as, for instance, providingsemantic context to relevant business data, providing suggestions to theuser to add semantic and/or social data, and generating a number ofranked suggestions to add semantic data to the workspace according tosocial criteria.

The illustrated business intelligence module 217, at a high level,provides software and/or middleware services for performance analytics(e.g., supports organizational efforts to develop sophisticated visualrepresentations of processes and performance, providing organizationswith new insights that can help them make more informed decisions,assess and plan a business intelligence strategy, deploy dashboardtools, generate management and operational reports, and build an ITinfrastructure that provides high scalability for users and data);services for analytic applications (e.g., provides guidance anddeployment expertise in implementing analytic applications, offeringpre-built analytics and data models to help a customer with a specificbusiness problem in various industries, helping organizations toefficiently deploy applications); and introductory business intelligenceservices (e.g., introduces organizations to the dynamics of usingbusiness intelligence, providing the ability to leverage thefunctionality of business intelligence—such as executive dashboards andoperational reports—without initiating a full-scale implementation).

The illustrated open source module 218, in some embodiments, may be anopen source web application framework, such as, for example, a Rails (orRoR) for the Ruby programming language. In some embodiments, the module218 may provide generic services like authentication, authorization,repository, logging, and otherwise.

The illustrated virtual appliance 215 is communicably coupled to abusiness object repository 235 in order to consume (e.g., retrieve,modify, manipulate or otherwise) business objects relevant to thebusiness enterprise. Each business object stored on the repository 235,for example, a capsule with an internal hierarchical structure, behavioroffered by its operations, and integrity constraints. In general, theoverall structure of the business object model ensures the consistencyof the interfaces that are derived from the business object model. Thederivation helps ensure that the same business-related subject matter orconcept can be represented and structured in the same way in variousinterfaces. The business object model defines the business-relatedconcepts at a central location for a number of business transactions. Inother words, it reflects the decisions made about modeling the businessentities of the real world acting in business transactions acrossindustries and business areas. The business object model is defined bythe business objects and their relationship to each other (the overallnet structure).

Business objects are generally semantically disjointed, i.e., the samebusiness information is represented once. In some embodiments, thebusiness objects are arranged in an ordering framework such that theycan be arranged according to their existence dependency to each other.For example, in a modeling environment, the customizing elements mightbe arranged on the left side of the business object model, the strategicelements might be arranged in the center of the business object model,and the operative elements might be arranged on the right side of thebusiness object model. Similarly, the business objects can be arrangedin this model from the top to the bottom based on defined order of thebusiness areas, e.g., finance could be arranged at the top of thebusiness object model with customer relationship management (CRM) belowfinance and supplier relationship management (SRM) below CRM. To helpensure the consistency of interfaces, the business object model may bebuilt using standardized data types as well as packages to group relatedelements together, and package templates and entity templates to specifythe arrangement of packages and entities within the structure.

A business object may be defined such that it contains multiple layers.Typical business object may contains four layers: a kernel layer, anintegrity layer, an interface layer, and an access layer. The innermostlayer of the example business object is the kernel layer. The kernellayer represents the business object's inherent data, containing variousattributes of the defined business object. The second layer representsthe integrity layer. The integrity layer contains the business logic ofthe object. Such logic may include business rules for consistentembedding in a computing environment and the constraints regarding thevalues and domains that apply to the business object. Business logic maycomprise statements that define or constrain some aspect of thebusiness, such that they are intended to assert business structure or tocontrol or influence the behavior of the business entity. It may pertainto the facts recorded on data and constraints on changes to that data.In effect, business logic may determine what data may, or may not, berecorded in business object. The third layer, the interface layer, maysupply the valid options for accessing the business object and describethe implementation, structure, and interface of the business object tothe outside world. To do so, the interface layer may contain methods,input event controls, and output events. The fourth and outermost layerof the business object is the access layer. The access layer defines thetechnologies that may be used for external access to the businessobject's data. Some examples of allowed technologies may includeCOM/DCOM (Component Object Model/Distributed Component Object Model),CORBA (Common Object Request Broker Architecture), RFC (Remote FunctionCall), Hypertext Transfer Protocol (HTTP) and Java, among others.Additionally, business objects of this embodiment may implement standardobject-oriented technologies such as encapsulation, inheritance, and/orpolymorphism.

The illustrated system 200 includes the in-memory database 220communicably coupled to the web portal 205 and the virtual appliance215. For example, as illustrated, the in-memory database 220 may beinvoked by either or both of the web portal 205 and the virtualappliance 215. The in-memory database 220 may be substantially similarto the in-memory database 140 described in FIG. 1. Further, thein-memory database 220 includes in-memory business logic 225 (which, asillustrated, may be invoked by either or both of the web portal 205 andthe virtual appliance 215). The in-memory business logic 225 may includea combination of one or more of software, middleware, and hardware (orother logic) operable to manipulate one or more data objects (e.g.,stored in-memory on the database 220). For instance, the in-memorybusiness logic 225 may perform functionality associated with and/or usedby the enterprise workspace (client) 210 and/or enterprise workspace(server) 216, such as the methods described with reference to FIGS. 5-7and 9, as well as other functionality disclosed herein.

The illustrated in-memory database 220 is communicably coupled to abusiness suite 240. In some embodiments, as described above, complete orpartial processing may be done directly on a database, thus renderingmaking components like the “database,” “server,” and/or “suite”substantially similar or identical. At a high level, the business suite240 may consist of one or more integrated enterprise applicationsenabling, e.g., business enterprises, to execute and optimize businessand IT strategies, such as performing industry-specific, andbusiness-support processes. In some embodiments, the business suite 240may be built on an open, service-oriented architecture (SOA). Suchintegrated business applications may be replicated, for example, via theweb portal 205 through the in-memory database 220, such as via theenterprise workspace (client) 210.

The illustrated in-memory database 220 is also communicably coupled tothird party content 245. The third party content 245, for example, maybe substantially similar to data stored and/or referenced by the remotecomputing system 135 described with reference to FIG. 1, such as, forexample, the third party content 135. In some aspects, the third partycontent 245 may be stored and/or accessed through relational databases(i.e., relational databases stored on magnetic memory that require aprocess, e.g., ETL, to transfer data from one system to another not inreal time but with a delay of an hour, day, week, or longer). Forinstance, while some business data may be stored on the in memorydatabase 220 and therefore, be accessible in real-time, some businessdata may be available to and/or exposed by the in-memory business logic225, such as the third party content 245.

The illustrated system 200 also includes one or more clients 230. Forinstance, as illustrates, the clients 230 may include a mobile client231, and a web workspace 232. One or more of the clients 230 may invoke,for example, the enterprise workspace (server) 216, on the virtualappliance 215 in order to access and/or expose the functionality of thein-memory business logic 220. For example, the mobile client 231 mayfacilitate invocation of the enterprise workspace (server) 216 from amobile device (e.g., cell phone, smart phone, email mobile device, PDA,or other mobile device). The web workspace 232 may, for instance,facilitate invocation of the enterprise workspace (server) 216 through anetwork connection, such as the global network known as the world wideweb.

Turning to FIG. 2B, the illustrated distributed computing system 250includes the web portal 205 communicably coupled to an in-memoryapplication server 255, which, in turn, is communicably coupled to theclients 230, the business object repository 235, and a businessfinancial management module 260. Accordingly, as illustrated, thedistributed computing system 250 may provide for an alternativedeployment configuration of a system operable to generate, view, modify,and/or otherwise manipulate a contextual workspace, as compared to thesystem 200, but with similar (or identical) components in some respect.For example, as illustrated, the in-memory application server 255 insystem 250 may replace the virtual appliance 215/in-memory database 220shown in FIG. 1. The in-memory application server 255, as illustrated,includes the in-memory database 220 (including, for example, thein-memory business logic 225, not shown here). The in-memory applicationserver 255, as illustrated, also includes the server enterpriseworkspace 216, the business intelligence module 217, and the open sourcemodule 218.

The illustrated business financial management module 260, generally,supports IT services costing, customer value analysis, cost to serve,bill of material costing, and activity based costing, among otherfinancial metrics. In some implementations, for example, the businessfinancial management module 260 may provide for user-friendly, rapid,and efficient model building; accurate calculation and cross-charge forIT and other shared services; monitoring and manage costs, pricing, andtrue profitability; accurate assignment of overheads and indirect coststo customers, products, and channels; access to cost and profitabilitydata by customer, product, or business channel; accurate assignment,monitoring, and analysis of overhead costs tied to bill of materials,among other functions.

FIG. 3 illustrates an example virtual distributed computing architecture300 operable to generate, view, modify, and/or otherwise manipulate acontextual workspace. As illustrated, the virtual distributed computingarchitecture 300 includes a web client 302, a web server 340, anin-memory computing engine 356, one or more external services 370, oneor more consumption channels 372, one or more external content providers374, and one or more data sources 376. As further illustrated, the webclient 302, the web server 340, the in-memory computing engine 356, theone or more external services 370, the one or more consumption channels372, the one or more external content providers 374, and/or the one ormore data sources 376 may be communicably coupled through an HTTP and/orREST architecture.

The illustrated web client 302 includes a UI module 304, a connectivitymodule 306, and a context module 308. The illustrated UI module 304, insome embodiments, may generate, modify, and/or maintain a virtualcontextual workspace and present the workspace to a user through a GUI.The UI module 304 includes a workspace manager 310, a module gallery312, a layout manager 314, a data orchestrator 316, a contextualassistance module 318, a global filtering module 322, a data streamingmodule 324, and a timeline module 326. The illustrated workspace manager310, in some embodiments, may include functionality that managesoperation and use of the contextual workspace by the user.

The illustrated module gallery 312, in some embodiments, may keep trackof one or more workspace modules (such as the workspace modules shown inFIGS. 4A-4C) viewable on and/or available to a contextual workspacepresented to the user. For example, the module gallery 312 may includean index (e.g., stored in a repository) of the current workspace modulesbeing viewed by the user in the contextual workspace.

The illustrated layout manager 314, in some embodiments, may determinehow one or more selected and/or requested workspace modules may bearranged on the contextual workspace. For example, the layout manager314 may determine a size and/or placement of each module on thecontextual workspace in order to, for instance, better manage thecontextual workspace in the GUI presented to the user.

The illustrated data orchestrator 316, in some embodiments, maydetermine, for example, one or more available views and/or functionalityavailable to the user with respect to a data cube (e.g., an OLAP cubestoring business data exposable to the user). For example, the dataorchestrator 316 may determine how to present or show any data model,including a data cube, to the user (e.g., icon, full screen, orsomething in between).

The illustrated contextual assistance module 318, in some embodiments,may include functionality that provides the user of the contextualworkspace generated by the architecture 300 with additional businessdata contextually relevant with business data being currently viewed inthe workspace. For instance, as described below with respect to FIG. 5,the contextual assistance module 318 (or other modules included in theillustrated architecture 300) may identify semantically relatedinformation to that currently viewed on the contextual workspace by theuser. The semantically related information may be suggested to the user,for example, to add to the contextual workspace through one or moreworkspace modules.

The illustrated global filtering module 320, in some embodiments, mayprovide for functionality that, for example, ranks and/or filtersvarious suggestions for content (e.g., semantically related content orsocially related content) to be added to the user's contextual workspacethrough architecture 300. For instance, as described below with respectto FIG. 9, the global filtering module 320 (or other modules included inthe illustrated architecture 300) may rank suggestions to addsemantically related data to the user's contextual workspace accordingto one or more social metrics.

The illustrated data annotations module 322, in some embodiments, mayprovide for functionality that, for example, allows a user to generatenotes (or other annotations) that provide specific context to datawithin one or more workspace modules in the contextual workspace. Forinstance the data annotations module 322 may, in some aspects, allow theuser to dynamically create and/or view insights (e.g., notes or otherannotations) on contexts of data within the contextual workspace. Theinsights could be for the user or other users (e.g., a collaboration).

The illustrated data streaming module 324, in some embodiments, maymanage, direct, and/or control the transfer of data (e.g., from dataobjects, data cubes, documents, reports, feeds, and other data sources)to, for example, the web client 302. For instance, the data streamingmodule 324 may manage the speed at which data is transferred to the webclient 302 to, for example, expose on one or more workspace modules ofthe contextual workspace generated and presented to the user.

The illustrated timeline module 326, in some embodiments, may providefor functionality that allows a user to “time travel” through one ormore previous views of a contextual workspace. For instance, in someembodiments, each distinct contextual workspace (i.e., each view with adistinct set, combination, and/or number of workspace modules includedwithin the contextual workspace) may be time stamped with a particulartime (e.g., hour/minute/day, date, week, month, or other granularity).The time stamp may be part of the metadata of the contextual workspacegenerated by and presented to the user. In some aspects, the user may“time travel” back in time to view previous versions of the contextualworkspace according to the time stamp. Thus, not only the data containedin the contextual workspace, but the user's subjective view on the data(e.g., which workspace modules were selected) can be retrieved. The usermay filter the contextual workspace versions by the time stamp, dataexposed in the contextual workspaces, keywords, and/or other metrics.

The illustrated connectivity module 306 includes, in some embodiments,context sources module 328 and a content fetching module 330, and, insome aspects, may provide connection functionality for the web client302 to communicate with, for example, the web server 340, the in-memorycomputing engine 356, the data sources 376, and/or other contentrepositories or sources. The illustrated context sources module 328, insome embodiments, may provide for functionality to the web client 302 toconnect to various sources of contextually relevant (e.g., semanticallyrelevant) data related to one or more workspace modules in thecontextual workspace. For example, semantically relevant data may beexposed from, for example, data objects, data cubes, documents, searchfunctions, social data (e.g., people), and other sources. In someembodiments, for example, the context sources module 328 may functionalong with other components of the illustrated architecture (e.g., thecontextual services manager 350, the social suggestions module 358, andother components) to perform the functionality described in FIGS. 5-7and 9, as well as other processes and methods described herein.

The illustrated content fetching module 330, in some embodiments, mayprovide functionality to retrieve and/or gather data content from one ormore data sources, such as, for example, data objects, data cubes,documents, search functions, social data (e.g., people), and othersources.

The illustrated context module 308 includes, in some embodiments, acontext discovery module 332, a suggestions and rankings module 334, anevents and alerts module 336, and a context model 338. In some aspects,the context module 308 may provide functionality related to discoveringand/or suggesting semantically relevant data (e.g., from data objects,data cubes, and otherwise) related to data being viewed by the user onthe contextual workspace. The illustrated context discovery module 332,in some embodiments, may provide functionality related to searching forand/or exposing semantically relevant content related to data on thecontextual workspace, e.g., in one or more workspace modules. Forexample, semantically relevant data may include information related todata that the user works with or views in the contextual workspaceand/or arranges (according to his/her expertise) within the contextualworkspace. For instance, the arrangement of data (e.g., which workspacemodules are selected and/or viewed) may provide at least a portion ofthe overall context of the workspace apart from the data (numerical orotherwise). The semantically relevant data may be related, e.g.,according to keywords, metadata, cube dimensions, cube measures, cubeviews, table headings, etc., which may be unknown to the user (due to,for instance, the vast amount of data available to the user).

The illustrated suggestions and rankings module 334, in someembodiments, may include functionality that provides suggestions ofsemantically relevant data to the user to add to the contextualworkspace. The suggestions and rankings module 334 may also includefunctionality that ranks a number of suggestions of semanticallyrelevant data according to, for example, matching keywords, matchingmetadata, social metrics, and/or other criteria. For instance, in someaspects, the suggestions and rankings module 334 may includefunctionality illustrated in one or more of the methods shown in FIGS.5-7 and 9.

The illustrated events and alerts module 336, in some embodiments, mayinclude functionality that allows the user set an alert, e.g., at agiven point of time when a particular event is occurring. The user,therefore, may be reminded (at a later time) of how that particularproblem was resolved. In some aspects, the events and alerts module 336may also provide a user with notification (via one or more workspacemodules of the contextual workspace) of events and/or alerts based on,for instance, actions of other users with similar roles within thebusiness enterprise (e.g., within the same organization unit, or havingthe same title).

The illustrated context model 338, in some embodiments, may build and/orgenerate a model (e.g., a context bag) containing the discoveredsemantically relevant data related to the contextual workspace dataexposed in one or more workspace modules.

The illustrated web server 340 includes a workspace services module 342.The illustrated workspace services module 342 includes a contentprovider manager 344, a workspace data manager 346, a context datamanager 348, a contextual services manager 350, and a data acquisitionand modeling module 354. The illustrated content provider manager 344and the illustrated workspace data manager 346, in some embodiments, maydirect, control, and/or otherwise manage the content and data exposed onthe one or more workspace modules in the contextual workspace.

The illustrated context data manager 348, in some embodiments, maydirect, control, and/or otherwise manage semantically relevant content(e.g., determined according to, for example, metadata contained in theworkspace modules on the contextual workspace). For example, the contextdata manager 348, either alone or along with one or more othercomponents of the architecture 300 (e.g., the semantic suggestionsmodule 360, the suggestions and rankings module 334, and/or othercomponents) may provide functionality illustrated in one or more of themethods shown in FIGS. 5-7 and 9. In some embodiments, the illustratedcontextual services manager 350 may include functionality that assists(at least partially) the context data manager 348 in directing,controlling, and/or otherwise managing semantically relevant.

The illustrated data acquisition and modeling module 354, in someembodiments, may perform modeling functionality on business data to beexposed in one or more workspace modules in the contextual workspace.For instance, the data acquisition and modeling module 354 may modeldata objects, such as data cubes, as three-dimensional workspace modulesin the contextual workspace.

The illustrated in-memory computing engine 356 includes a socialsuggestions module 358, a semantic suggestions module 360, an alertengine 362, a data annotations module 364, a context database 366, and abusiness suite database 368. In some embodiments, the social suggestionsmodule 358 may, in some embodiments, include functionality that providessuggestions of socially relevant data to the user to add to thecontextual workspace. For instance, the suggestions may include businessdata that other users (e.g., other business users in the sameorganizational unit of the enterprise, same team of the user, or samerole of the user, to name a few) found relevant and/or useful. Thesuggested business data may also be contextually relevant to one or moreworkspace modules active in the user's contextual workspace. The socialsuggestions module 358 may, in some aspects, include functionalityillustrated in one or more of the methods shown in FIGS. 5-7 and 9.

The illustrated semantic suggestions module 360, in some embodiments,include functionality that provides suggestions of semantically relevantdata to the user to add to the contextual workspace. For instance, thesuggestions may include business data that has some related contextualsimilarity with data exposed in one or more workspace modules in theuser's contextual workspace. For example, the semantically relevant datamay be business data from a data cube having one or more similardimensions and/or measures as compared to a data cube currently beingviewed and/or analyzed in the contextual workspace. As another example,the semantically relevant data may be business data from a table havinga similar heading to a table currently being viewed in the contextualworkspace. Thus, the suggested semantically relevant data may bebusiness data that provides insight to the user that the business userwould not have known about otherwise. The semantic suggestions module360 may, in some aspects, include functionality illustrated in one ormore of the methods shown in FIGS. 5-7 and 9.

The illustrated alert engine 362, in some embodiments, in someembodiments, may include functionality that allows the user set analert, e.g., at a given point of time when a particular event isoccurring. The user, therefore, may be reminded (at a later time) of howthat particular problem was resolved. In some aspects, the alert engine362 may also provide a user with notification (via one or more workspacemodules of the contextual workspace) of alerts based on, for instance,actions of other users with similar roles within the business enterprise(e.g., within the same organization unit, or having the same title).

The illustrated data annotations module 364, in some embodiments, mayprovide for functionality that, for example, allows a user to generatenotes (or other annotations) that provide specific context to datawithin one or more workspace modules in the contextual workspace. Forinstance the data annotations module 364 may, in some aspects, allow theuser to dynamically create and/or view insights (e.g., notes or otherannotations) on contexts of data within the contextual workspace. Theinsights could be for the user or other users (e.g., a collaboration).

The illustrated context database 366, in some embodiments, may storeand/or reference one or more contextually relevant data objects, such asdata objects that have been determined (e.g., by one or more componentsof the architecture 300 such as the social suggestions module 358, thesemantic suggestions module 360, or other component) to be sociallyand/or semantically relevant based on one or more workspace modules inthe contextual workspace. For example, the context database 366 maystore and/or reference one or more data cubes, reports, documents,business objects, or other data objects. In some embodiments, thecontext database 366 may also store and/or reference one or moresuggestions (e.g., social, semantic, or otherwise) generated by thearchitecture 300.

The illustrated business suite database 368, in some embodiments, maystore and/or reference business data, such as business data utilized bythe business suite app 374 a.

The illustrated external services 370 include an event insight 370 a,social intelligence 370 b, text analysis 370 c, and feed services 370 d.Generally, the illustrated external services 370 may provide businessdata (e.g., semantically and/or socially relevant business data) to atleast one of the web client 302, the web server 340, and/or in-memorycomputing engine 356. For instance, the illustrated event insight 370 a,in some embodiments, may provide alerts to the web client 302 and/or webserver 340 to present to the business user through one or more workspacemodules of the contextual workspace. The illustrated social intelligence370 b, in some embodiments, may provide social business data (e.g.,social connections between one or more business users in a businessenterprise) to the web client 302 and/or web server 340 to present tothe business user through one or more workspace modules of thecontextual workspace. For example, the social business data may be agraph and/or chart showing business connections between employees, suchas, for instance, a degree of connection between the employees (e.g.,through one or more known business contacts). The illustrated textanalysis 370 c, in some embodiments, may identify business data locatedin documents or other data objects through, for instance, a keywordsearch or metadata search of the text in the documents. The illustratedfeed services 370 d, in some embodiments, may provide business datathrough one or more web feeds to the web client 302 and/or web server340 (e.g., RSS feeds).

The illustrated consumption channels 372 include a mobile channel 372 aand one or more feed clients 372 c. Typically, the consumption channels372 may also provide business data (e.g., semantically and/or sociallyrelevant business data) to at least one of the web client 302, the webserver 340, and/or in-memory computing engine 356. For instance, theillustrated mobile channel 372 a, in some embodiments, may providebusiness to the web client 302 and/or web server 340 from one or moremobile channels to present to the business user through one or moreworkspace modules of the contextual workspace.

The illustrated feed clients 372 c, in some embodiments, may include,for example, a mail client, which may provide business data to at leastone of the web client 302, the web server 340, and/or in-memorycomputing engine 356 through one or more electronic communications.

The illustrated external content providers 374 include one or more UIapplications, such as a business suite app 374 a, a businessintelligence app 374 b, and a social gadget app 374 c. Typically, theexternal content providers 374 provide for a number of UI applicationsthat may be interfaced by a user, e.g., through the web client 302 orotherwise. The business suite app 374 a, for example, may be businessapplications that provide integration of information and processes,collaboration, industry-specific functionality, and scalability. Forinstance, the business suite app 374 a may include one or moreconstituents, such as CRM (Customer Relationship Management), ERP(Enterprise Resource Planning), PLM (Product Lifecycle Management), SCM(Supply Chain Management), and/or SRM (Supplier RelationshipManagement). The business intelligence app 374 b, for example, mayprovide for a UI application that may be used in identifying andextracting business data (e.g., ETL) and analyzing such business data.The business data may include, for example, sales revenue, profits,and/or costs broken down by products and/or departments. In someaspects, the business intelligence app 374 b may provide for historical,current, and/or predictive views of business operations, such asreporting, online analytical processing, analytics, data mining, processmining, business performance management, benchmarking, text mining,and/or predictive analytics. In some aspects, the business intelligenceapp 374 b may also provide for layers including an ETL layer (e.g.,responsible for extracting data from a specific source, applyingtransformation rules, and loading it into a data warehouse); the datawarehouse (e.g., responsible for storing the information in varioustypes of structures); reporting (e.g., responsible for accessing theinformation in the data warehouse and presenting it in a user-friendlymanner to the business user); and a planning and analysis layer (e.g.,provides capabilities for the user to run simulations and perform taskssuch as budget calculations). The illustrated social gadget app 374 c,for example, may include games, social news feeds and other UI apps.

The illustrated data sources 376 include an in-memory database 376 a, agateway 376 b, one or more feeds 376 c, and one or more documents 376 d.Typically, the data sources 376 may provide data, such as business data,to the illustrated web client 302 and/or web server 340 that may bemanipulated to, for instance, provide suggestions of additionalcontextual data, provide suggestions of semantically-related data,and/or provide other suggestions, alerts, or events. For example, thein-memory database 376 a may store and/or reference one or more datacubes containing business data. The gateway 376 b may, for example, be afront-end server to the business suite app 374 and expose business suiteservices in an open protocol format. The feeds 376 c, for instance, mayprovide business and/or social data through social updates, for example,Twitter, Facebook, RSS feed/channels, and/or other feed sources orchannels. The documents 376 d, for instance, may be any number ofdocuments stored in or referenced by a document store (i.e., in arelational database or otherwise).

FIGS. 4A-4B illustrate example virtual contextual workspaces 400, 425,and 450, respectively, having one or more workspace modules. In someembodiments, one or more of the workspace modules (e.g., workspacemodules 405, 410, 415, and 420 illustrated in FIG. 4A) may be acombination of iFrames and/or Div frames, which may expose underlyingbusiness data, e.g., from one or more data sources illustrated in FIGS.1, 2A-2B, and/or 3. Further, the illustrated workspace modules mayinclude or describe data that is contextually relevant and/or relatedwithin the virtual contextual workspace.

Turning to FIG. 4A, the illustrated virtual contextual workspaces 400includes workspace modules 405, 410, 415, and 420. The illustratedworkspace module 405, for example, shows a module including a bar graph,e.g., of travel spending by time period. The illustrated workspacemodule 410, for example, shows a module including a bar graph, e.g., ofthird party spending finances by time period. The illustrated workspacemodule 415, for example, shows a module including a list of alert feeds,e.g., of alerts related to travel spending and/or travel requests of abusiness enterprise. The illustrated workspace module 420, for example,shows a module including a pie graph, e.g., of development spending ofan organizational unit of the enterprise during a specific timeduration.

As illustrated, one or more of the workspace modules 405, 410, 415, and420 may be semantically related. In other words, one or more of theworkspace modules 405, 410, 415, and 420 may display and/or exposebusiness data that is related by some specific context. For instance,semantically related data may be arranged in the workspace modules 405,410, 415, and 420 according to how the business user works on thecontextual workspace 400 (e.g., how the user puts things together(according to his/her expertise)). While the user may not know all theavailable data, there may be a context to the workspace 400 that he/sheunderstands. This context, for instance, could be keywords, metadata,dimensions/slices of data, and otherwise. While other users (even otherusers within the same business enterprise, same organization unit, orotherwise) may not understand the context of the workspace 400, the userunderstands the desired context. The user, however, likely does not know(or is not aware of) all of the data that is available for this context.

Turning to FIG. 4B, the illustrated virtual contextual workspaces 425includes workspace modules 430, 435, 440, and 445. The illustratedworkspace module 430, for example, shows a module including a table,e.g., of entries for travel spending by employee. The illustratedworkspace module 435, for example, shows a module including insights,e.g., of an enterprise employee and/or user of the contextual workspace425 providing business insights related to the workspace. For example,module 435 shows insights related to business employee travel spendingand financial data. The illustrated workspace module 440, for example,shows a module including a list of links, e.g., of websites (externaland internal), contact names, and/or forms, relevant to the contextualworkspace 425. The illustrated workspace module 445, for example, showsa module including a bar graph, e.g., of spent travel budget for theenterprise during a specific time duration.

Turning to FIG. 4C, the illustrated virtual contextual workspaces 450includes workspace modules 455, 465, 470, and 475. The illustratedworkspace module 455, for example, shows a module including a pie graph,e.g., of travel spending by vendor, along with a suggestion 460 to mergedifferent types of documents (here, invoices and purchase orders). Theillustrated workspace module 465, for example, shows a module includinginsights, e.g., of websites (external and internal) that may providesome contextually relevant data for the workspace 465. The illustratedworkspace module 470, for example, shows a module including a bar graph,e.g., of travel spending by entity (here a third party). The illustratedworkspace module 475, for example, shows a module including a table,e.g., of entries for vendor contact information.

FIG. 5 illustrates an example method 500 for using a contextualworkspace. For example, in some embodiments, method 500, whenimplemented, may identify semantically related information to thatviewable on the contextual workspace by a user. In some embodiments,method 500 may be performed on any of the illustrated computingenvironments of FIGS. 1, 2A, 2B, and 3, as well as other appropriatecomputing environments. Method 500 may be performed, at least in part,by the web client 302, the web server 340, the in-memory computingengine 356, and/or a combination thereof, e.g., by the contextualassistance module 318, the contextual services manager 350, and/or othercomponents of architecture 300 (“contextual services modules”). Method500 may begin at step 502, when the web client 302, the web server 340,and/or the in-memory computing engine 356 generate a virtual workspaceviewable by a user on a graphical user interface. For example, in someembodiments, the virtual workspace may be one of the example contextualworkspaces illustrated in FIGS. 4A-4C.

In step 504, the contextual services modules identify an interaction bythe user with workspace data contained in data objects. The userinteraction with workspace data in the contextual workspace may beidentified in a number of ways. For example, the interaction may be anactivation of a particular workspace module within the contextualworkspace (e.g., making the particular module the active workspacemodule). As another example, the interaction may be a placement of auser pointer (e.g., a cursor) above (i.e., coincident with) a locationof a particular workspace module in the contextual workspace. Forinstance, the user may “hover” the cursor over the particular module. Asyet another example, the interaction may include an identification of akeyword (e.g., text or metadata) associated with one or more workspacemodules in the contextual workspace). For instance, the user mayhighlight, select, and/or otherwise identify (e.g., through a search)the keyword within the contextual workspace. As another example, theinteraction may include a selection of one or more data objects (e.g.,text, business objects, data cubes, workspace modules, or otherwise)exposed and/or presented in the contextual workspace (e.g., through oneor more workspace modules).

In addition, the interaction may be a manipulation of a data object,such as a data cube. For example, the manipulation may include, amongothers, an identification of a dimension, measure, and/or view of thedata cube. The manipulation may also include, for instance, slicing,dicing, and/or pivoting the data cube.

If the identified interaction is an active module of the virtualworkspace, then the contextual services modules identify a descriptionof the active workspace module in step 506. In some aspects, thedescription may be the title and/or heading of the active workspacemodule. In other aspects, the description may be a metadata descriptionof the active workspace module, not shown to the user through thecontextual workspace. For example, if the active workspace module is areport, the description may be the title of the report. Next, thecontextual services modules search for data associated with thedescription of the active workspace module in step 514. For instance,the contextual services modules may search for related data in dataobjects, e.g., stored in a repository such as an in-memory database,that is semantically related to the identified description. For example,the semantically related data may contain and/or have the samedescription as the active workspace module (e.g., the same title).

If the identified interaction is a keyword in the virtual workspace,then the contextual services modules search the data contained in thedata objects (e.g., other data cubes stored in an in-memory database)for the keyword in step 508. As one example, the keyword, which may bemetadata, may refer to a particular supplier, vendor, customer, and/orclient of a business enterprise. The searched data objects, therefore,may include all (or a portion) of the stored data objects referring toand/or including the particular supplier, vendor, customer, and/orclient of a business enterprise.

If the identified interaction is user selection, then the contextualservices modules search for data contained in the data objects (e.g.,other data cubes stored in an in-memory database) associated with theuser selection in step 510. As one example, the user selection may beparticular text contained in a report (e.g., text describing a specificproduct). The searched data objects, therefore, may include all (or aportion) of the stored data objects referring to and/or including theparticular text selection regarding the product.

If the identified interaction is a data cube manipulation, then thecontextual services modules identify an action by the user manipulatingthe data cube in step 512. The manipulation may include, for instance,an identification of a particular portion of the data cube, e.g., ameasure, dimension, view, or otherwise. The manipulation may alsoinclude, for instance, an action performed on the data cube, such as aslice, dice, pivot, or otherwise. In identifying the manipulation, thecontextual services modules may also search and identify other datacubes having, for example, similar measures, dimensions, and/or views asthe data cube manipulated by the user.

Regardless of the identified interaction, the contextual servicesmodules identify additional semantically-related data contained in theone or more data objects in step 516. The semantically-related (e.g.,relevant) data may include, for instance, additional data cubes havingsimilar views, measure, dimensions, or otherwise. The data may alsoinclude other data objects having the same, e.g., keywords,descriptions, titles, metadata, text, as that identified by the businessuser through the contextual workspace.

Method 500 may include other aspects as well. For instance, in someaspects, the identified semantically relevant data may be stored(separately or otherwise). In some further aspects as described above,the searched data objects may be stored in an in-memory database (e.g.,the in-memory database 140), thereby allowing real-time searching andidentification of the semantically relevant data.

FIG. 6 illustrates another example method 600 for using a contextualworkspace. For example, in some embodiments, method 600, whenimplemented, may provide a suggestion to a user of the contextualworkspace to add semantically related information to the contextualworkspace of the user. In some embodiments, method 600 may be performedon any of the illustrated computing environments of FIGS. 1, 2A, 2B, and3, as well as other appropriate computing environments. Method 600 maybe performed, at least in part, by the web client 302, the web server340, the in-memory computing engine 356, and/or a combination thereof,e.g., by the suggestions and rankings module 334, the semanticsuggestions module 360, other components, and/or a combination thereof(“semantic suggestions modules”). Method 600 may begin at step 602, whenthe web client 302, the web server 340, and/or the in-memory computingengine 356 generate a virtual workspace viewable by a user on agraphical user interface. For example, in some embodiments, the virtualworkspace may be one of the example contextual workspaces illustrated inFIGS. 4A-4C.

In step 604, the semantic suggestions modules identify an interaction bythe user with workspace data contained in data objects. The interactionby the user may be a variety of different interactions. For instance,the interaction may be an identification of a workspace module added tothe virtual contextual workspace by the user. The interaction may alsobe an identification of a selection of a workspace module among severalworkspace modules on the user's contextual workspace. As anotherexample, the interaction may be an identification of a selection of datacontained in the particular data object by the user, such as, forexample, a selection of text in a document, a selection of a heading ina table, and/or a selection of an axis or data point in a graph. As yetanother example, the interaction may be an identification of a metric ofa particular data object in a workspace module among several metrics.

In some embodiments, one or more data objects viewable and/or manageablewithin the workspace modules in the contextual workspace may be datacubes. In such embodiments, the interaction by the user may be aninteraction with at least some of the data contained in a particulardata cube of the data cubes exposed in one or more workspace moduleswithin the contextual workspace. For example, the user may interact(e.g., select, modify, slice, dice, pivot, or otherwise) a particularmeasure, dimension, and/or view of the data cube.

In step 606, the semantic suggestions modules identify data contained inthe data objects that is semantically related to the data interactedwith by the user. For instance, in some embodiments, step 606 mayinclude identifying metadata associated with the data interacted with bythe user. The metadata may be contained in additional data objects,e.g., data objects not actively being viewed and/or managed in thecontextual workspace. For example, metadata that is part of the datainteracted with by the user may be searched for and matched to metadatain other data objects stored in a repository communicably coupled to thecontextual workspace (e.g., in an in-memory database). In some aspects,step 606 may also include matching an identified metric of the datainteracted with by the user to similar (or identical) metric(s) of datain the stored data objects.

In the case of the data objects being data cubes (e.g., OLAP cubes),step 606 may include searching the stored data cubes for another datacube having an identified metric of the particular data cube interactedwith by the user. For example, step 606 may include querying the storeddata cubes for at least one view, dimension, and/or measure of data thatmatches a corresponding view, dimension, and/or measure of datacontained the interacted with workspace modules in the contextualworkspace. As another example, step 606 may include searching the storeddata cubes for at least one report utilizing the view of data thatmatches a view of data contained in at least one workspace moduleinteracted with by the user.

In step 608, the semantic suggestions modules generate suggestions forthe semantically-related data contained in the data objects. One or moreof the generated suggestions may include a description of data containedin the data objects that is semantically related to the data interactedwith by the user. For instance, once at least some semantically related(e.g., semantically relevant) data is identified in step 606,suggestions for each discrete portion of data (e.g., data cube, report,document, or other type of object that contains business data) may begenerated and, in some cases, stored in a repository (e.g., thein-memory database 140, a local client memory, and/or a server memory).The description of data may be, for example, a description of the dataobject itself (e.g., name of data cube, name of report, etc.); adescription of metadata in the data object; a description of a metric ofthe data object (e.g., a view, a measure, a dimension of a data cube, ora heading of a table, etc.).

In step 610, the semantic suggestions modules rank suggestions based ona calculated relevancy of each of the suggestions. For example, in someaspects, step 610 may include determining, for each data objectidentified in step 606, an occurrence frequency of at least one keywordfrom the metadata associated with the data interacted with by the userin the data contained in the identified data objects. Further, step 606may also include determining an order of the identified data objectsbased on the determined occurrence frequencies. In some aspects, a dataobject among the identified data objects that has the highest occurrencefrequency is at a top of the order of ranked suggestions. A data objectamong the identified data objects that has the lowest occurrencefrequency is at a bottom of the order, and so on.

In step 612, the semantic suggestions modules provide at least onesuggestion having a description of the semantically-related data to theuser. The suggestion(s) may be provided in a variety of differentmanners, such as, for example, a pop-up workspace module in thecontextual workspace, a feed stream within the contextual workspace,and/or an electronic communication to the user (e.g., via the contextualworkspace, via an email, via a text message, or otherwise).

Method 600 may include other aspects as well. For instance, in someaspects, the identified semantically relevant data and/or suggestions(ranked or otherwise) may be stored (separately or otherwise). In somefurther aspects as described above, the searched data objects and/orsuggestions for semantically relevant content may be stored in anin-memory database (e.g., the in-memory database 140), thereby allowingreal-time searching and identification of the semantically relevant dataand/or suggestions.

FIG. 7 illustrates another example method 700 for using a contextualworkspace. For example, in some embodiments, method 700, whenimplemented, may provide a suggestion to a user of the contextualworkspace to add data to the contextual workspace of the user accordingto one or more social metrics. In some embodiments, method 700 may beperformed on any of the illustrated computing environments of FIGS. 1,2A, 2B, and 3, as well as other appropriate computing environments.Method 700 may be performed, at least in part, by the web client 302,the web server 340, the in-memory computing engine 356, and/or acombination thereof, e.g., by the suggestions and rankings module 334,the social suggestions module 360, and/or a combination thereof (“socialsuggestions modules”). Method 700 may begin at step 702, when the webclient 302, the web server 340, and/or the in-memory computing engine356 generate a virtual workspace viewable by a user on a graphical userinterface. For example, in some embodiments, the virtual workspace maybe one of the example contextual workspaces illustrated in FIGS. 4A-4C.

In step 704, the social suggestions modules identify a modification ofthe virtual workspace by the user. The modification may be, for example,an addition of a workspace module to the contextual workspace by theuser. The modification may also be, for example, a deletion of aworkspace module from the contextual workspace by the user. Turning toFIG. 8A, for example, graphical view 805 shows a user of a contextualworkspace started with workspace module “M1” being added to theworkspace, followed by workspace module “M2” and then “M3.” In someaspects, each addition of a workspace module (e.g., M1, M2, and M3) maybe identified.

In step 706, the social suggestions modules compare the modified virtualworkspace with a plurality of stored virtual workspace states. Forexample, in some embodiments, step 706 may include identifying one ormore stored virtual workspace states that include a particular workspacemodule on the modified contextual workspace (e.g., M2 and/or M3). Thesocial suggestions modules may then determine one or more additionalworkspace modules within the identified stored virtual workspace statesthat is different from the particular workspace module. For instance,turning to FIG. 8A, graphical view 810 shows several stored workspacestates 815 (e.g., based on other users' contextual workspaces atcorresponding time instances). One stored workspace state 815 includesthe workspace modules M1, M4, and M5. A second stored workspace state815 includes the workspace modules M1, M7, and M9. A third storedworkspace state 815 includes the workspace modules M2, M3, and M9. Afourth stored workspace state 815 includes the workspace modules M2, M5,and M6.

Step 706 may also include determining a number of occurrences of each ofthe one or more additional workspace modules in the identified storedvirtual workspace states. For example, turning to FIG. 8A, graphicalview 820 shows a tally of the number of occurrences of each of theadditional workspace modules that are not in the contextual workspace ofthe user (e.g., M4, M5, M6, M7, M8, and M9). For example, workspacemodule M9 occurred three instances in other users' contextual workspacesthat also included at least one workspace module in common with themodified contextual workspace of the user. As illustrated in graphicalview 810, workspace module M9 occurred once in a contextual workspacewith workspace module M1, once in a contextual workspace with workspacemodule M2, and once in a contextual workspace with workspace module M3,for a total of three occurrences.

In step 708, the social suggestions modules determine a plurality ofsuggestions to add at least one workspace module to the modified virtualworkspace. In some aspects, this step may also include associating aranking number with each of the additional workspace modules (e.g., M4,M5, M6, M7, M8, and M9) in the identified stored virtual workspacestates (e.g., workspace states 815) based on the corresponding number ofoccurrences of each of the additional workspace modules. For instance,as illustrated in graphical view 820 of FIG. 8A, the additionalworkspace modules M4, M5, M6, M7, M8, and M9 are ranked according to thetally described above. The suggestions to the user may, therefore, be toadd workspace module M9, followed by workspace module M5, and so on.

In some aspects, step 708 may also include identifying a primary userrole of the user. For instance, as illustrated in graphical view 805 ofFIG. 8B, a role 825 of the user is determined (e.g., from employeerecords or otherwise). Further, user roles associated with the storedvirtual workspace states are determined. For instance, as illustrated ingraphical view 810 of FIG. 8B, roles 830 of the users of the storedworkspace states 815 are determined.

In some aspects, the ranking numbers of each of the additional workspacemodules (e.g., M4, M5, M6, M7, M8, and M9) may be determined (orrecalculated) according to the roles of the user(s). For instance, step708 may also include determining a number of occurrences of each of theadditional workspace modules within the stored virtual workspace states815 with a user role 830 that matches the user role 825. Step 708 mayalso include incrementing the ranking number associated with each of theadditional workspace modules in the stored virtual workspace statesbased on the corresponding number of occurrences of each of theadditional workspace modules. For example, as shown in graphical view835 of FIG. 8B, the workspace modules (e.g., M4, M5, M6, M7, M8, and M9)are re-ranked based on the determined roles. For example, asillustrated, the tally for workspace module M5 is increased by two basedon this module being included in the stored workspace states 815 of twoother sales persons in the business enterprise.

In some aspects, step 708 may also include determining a time durationbetween a modification of the stored virtual workspace state 815associated with a workspace module of the user's contextual workspace(e.g., M1, M2, M3) and a modification of the stored virtual workspacestate 815 associated with an additional workspace module (e.g., M4, M5,M6, M7, M8, or M9). For example, as shown in graphical view 840 in FIG.8C, time durations are determined between, for example, M1 and M4, M1,and M5, etc. The time duration, in some aspects, represents the amountof time between the addition of a particular workspace module (e.g., M4)and the addition of an initial workspace module (e.g., M1) to the storedworkspace state. In some aspects, for instance, a shorter time durationmay represent a higher importance of the added workspace module.

Step 708 may also include determining a corresponding weighted value forthe additional workspace module based on the determined time durations.For instance, as shown in graphical views 840 and 845, time durationranges may be associated with weighted values to, for example, representa relative importance or relevance of each of the workspace modules inthe stored workspace states 815. For example, a time duration less thanabout 1 day (e.g., an amount of time after one workspace module wasadded that another workspace module was added (or deleted or otherwisemodified)) may be weighted 2. A time duration between about 1 day and 5days may be weighted 1. A time duration greater than 5 days may beweighted 0 (i.e., no relative importance).

Based on the weighted values assigned to each additional workspacemodule (e.g., M4, M5, M6, M7, M8, and M9) of the stored workspace states815, step 708 may also include incrementing the ranking numberassociated with the additional workspace modules in the stored virtualworkspace states 815. For example, as shown in graphical view 845 inFIG. 8C, the tallies of the additional workspace modules may beincreased by the weighted values corresponding to the determined timedurations. While graphical view 845 in FIG. 8C shows a change in talliesrelative to graphical view 820 in FIG. 8A, the tallies may also beincremented (alternatively) based on the tallies shown in graphical view820 in FIG. 8B.

In step 710, the social suggestions modules provide one or more of thesuggestions to the user, e.g., based on the ranked suggestions.

Method 700 may include other aspects as well. For instance, in someaspects, the identified semantically relevant data and/or suggestions(ranked or otherwise) may be stored (separately or otherwise). In somefurther aspects as described above, the searched data objects and/orsuggestions for semantically relevant content may be stored in anin-memory database (e.g., the in-memory database 140), thereby allowingreal-time searching and identification of the semantically relevant dataand/or suggestions.

FIG. 9 illustrates another example method 900 for using a contextualworkspace. For example, in some embodiments, method 900, whenimplemented, may provide one or more suggestions to a user of thecontextual workspace to add semantically related information to thecontextual workspace that are ranked according to one or more socialmetrics. In some embodiments, method 900 may be performed on any of theillustrated computing environments of FIGS. 1, 2A, 2B, and 3, as well asother appropriate computing environments. Method 900 may be performed,at least in part, by the web client 302, the web server 340, thein-memory computing engine 356, and/or a combination thereof, e.g., bythe suggestions and rankings module 334, the semantic suggestions module360, the social suggestions module, other components, and/or acombination thereof (“rankings modules”). Method 900 may begin at step902, when the web client 302, the web server 340, and/or the in-memorycomputing engine 356 generate a virtual workspace viewable by a user ona graphical user interface. For example, in some embodiments, thevirtual workspace may be one of the example contextual workspacesillustrated in FIGS. 4A-4C.

In step 904, the rankings modules identify an interaction by the userwith workspace data contained in data objects in a business enterprise.The interaction by the user may be a variety of different interactions.For instance, the interaction may be an identification of a workspacemodule added to the virtual contextual workspace by the user. Theinteraction may also be an identification of a selection of a workspacemodule among several workspace modules on the user's contextualworkspace. As another example, the interaction may be an identificationof a selection of data contained in the particular data object by theuser, such as, for example, a selection of text in a document, aselection of a heading in a table, and/or a selection of an axis or datapoint in a graph. As yet another example, the interaction may be anidentification of a metric of a particular data object in a workspacemodule among several metrics.

In some embodiments, one or more data objects viewable and/or manageablewithin the workspace modules in the contextual workspace may be datacubes. In such embodiments, the interaction by the user may be aninteraction with at least some of the data contained in a particulardata cube of the data cubes exposed in one or more workspace moduleswithin the contextual workspace. For example, the user may interact(e.g., select, modify, slice, dice, pivot, or otherwise) a particularmeasure, dimension, and/or view of the data cube.

In some aspects, step 904 may also include identifying data contained inone or more data objects that is semantically related to the datainteracted with by the user. For instance, in some embodiments, step 904may include identifying metadata associated with the data interactedwith by the user. The metadata may be contained in additional dataobjects, e.g., data objects not actively being viewed and/or managed inthe contextual workspace. For example, metadata that is part of the datainteracted with by the user may be searched for and matched to metadatain other data objects stored in a repository communicably coupled to thecontextual workspace (e.g., in an in-memory database). In some aspects,step 904 may also include matching an identified metric of the datainteracted with by the user to similar (or identical) metric(s) of datain the stored data objects.

In the case of the data objects being data cubes (e.g., OLAP cubes),step 904 may include searching the stored data cubes for another datacube having an identified metric of the particular data cube interactedwith by the user. For example, step 904 may include querying the storeddata cubes for at least one view, dimension, and/or measure of data thatmatches a corresponding view, dimension, and/or measure of datacontained the interacted with workspace modules in the contextualworkspace. As another example, step 904 may include searching the storeddata cubes for at least one report utilizing the view of data thatmatches a view of data contained in at least one workspace moduleinteracted with by the user.

In step 906, the rankings modules generate a plurality of suggestionswith data that is semantically related to the data interacted with bythe user. One or more of the generated suggestions may include adescription of data contained in the data objects that is semanticallyrelated to the data interacted with by the user. For instance, once atleast some semantically related (e.g., semantically relevant) data isidentified in step 904, suggestions for each discrete portion of data(e.g., data cube, report, document, or other type of object thatcontains business data) may be generated and, in some cases, stored in arepository (e.g., the in-memory database 140, a local client memory,and/or a server memory). The description of data may be, for example, adescription of the data object itself (e.g., name of data cube, name ofreport, etc.); a description of metadata in the data object; adescription of a metric of the data object (e.g., a view, a measure, adimension of a data cube, or a heading of a table, etc.).

In step 908, the rankings modules determine a relationship of thesemantically related data to the business enterprise. In some aspects,step 908 may include determining a role of an employee in the businessenterprise that has used a particular portion of the semanticallyrelated data. Step 908 may also include, in some embodiments,determining the role based on, for instance, the user's identificationwithin his/her business enterprise as determined in the enterprise'scorporate human resource (HR) system(s). Determining the role may alsoinclude dynamically determining a business network relationship of theuser within the enterprise based on, for instance, participation inparticular projects, having particular expertise, participation in theparticular working groups, and other business interactions. For example,in some cases, other business users, i.e., other employees of a businessenterprise have used (e.g., viewed, manipulated, or otherwise) the datathat is semantically related to the interacted with data in the user'scontextual workspace. Step 908 may also include determining a businessunit of the business enterprise related to a particular portion of thesemantically related data. For instance, the semantically related datamay have a particular relationship with an organizational unit (e.g., ateam, a business group, a division, a company, a subsidiary, orotherwise) of the business enterprise. For instance, the semanticallyrelated data may be associated with a particular product and/or serviceoffered by a subsidiary of the business enterprise.

In step 910, the rankings modules compare the relationship of thesemantically related data to the role of the user in the businessenterprise. In some aspects, the rankings modules may compare the roleof the user (e.g., a position within the business enterprise)interacting with the contextual workspace with the role of the employeein the business enterprise that has used the portion of the semanticallyrelated data. If the role of the user and the role of the employeematch, for example, a rank of a suggestion describing the semanticallyrelated data may be increased (i.e., moved higher in importance on ascale of ranked suggestions). Further, if the role of the user and therole of the employee do not match but are similar (e.g., within adjacentlevels of seniority of the same position), the rank of the suggestiondescribing the semantically related data may also be increased, but to alesser degree.

As another example, the rankings modules may compare the role of theuser (as determined above) with the business unit of the businessenterprise related to a particular portion of the semantically relateddata. For example, the role of the user interacting with data in thecontextual workspace may fit within the business unit of the enterprise,e.g., be part of a group, team, or other unit within the particularorganizational structure of the enterprise, be working on a particularproject or job, or have the same or similar expertise as other users inthe business unit. If the role of the user fits within the businessunit, a rank of a suggestion describing the semantically related datamay be increased (i.e., moved higher in importance on a scale of rankedsuggestions).

In step 912, the rankings modules rank the plurality of suggestionsbased on the comparison of the relationship of the semantically relateddata to the role of the user. For example, certain suggestionsdescribing semantically related data may be ranked higher due to asocial position of the user within the business enterprise. For example,suggestions for semantically related data that has been viewed,modified, and/or used by other employees within the business enterprisehaving the same (or similar) role may be ranked higher than, forinstance, suggestions for semantically related data that has beenviewed, modified, and/or used by other employees within the businessenterprise having different roles than the user. Further, suggestionsfor semantically related data that is related to the user'sorganizational unit within the business enterprise (e.g., the samegroup, team, division, etc.) may be ranked higher than, for instance,suggestions for semantically related data that is related to anorganizational unit within the business enterprise different than theuser's. As another example, suggestions for semantically related datamay be based on, for instance, dynamically determined business networkrelationships of the user (e.g., within the entire enterprise, the samegroup, team, division, etc.). In step 914, the rankings modules presentat least a portion of the ranked suggestions to the user.

Method 900 may include other aspects as well. For instance, in someaspects, the identified semantically relevant data and/or rankedsuggestions may be stored (separately or otherwise). In some furtheraspects as described above, the searched data objects and/or suggestionsfor semantically relevant content may be stored in an in-memory database(e.g., the in-memory database 140), thereby allowing real-time searchingand identification of the semantically relevant data and/or rankedsuggestions.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made. For example, othermethods described herein besides those or in addition to thoseillustrated in FIGS. 5-7 and 9 may be performed. Further, theillustrated steps of methods 500, 600, 700, and 900 may be performed indifferent orders, either concurrently or serially. Further, steps may beperformed in addition to those illustrated in methods 500, 600, 700, and900, and some steps illustrated in methods 500, 600, 700, and 900 may beomitted without deviating from the present disclosure. Accordingly,other implementations are within the scope of the following claims.

What is claimed is:
 1. A method performed with a computing system formanaging a virtual workspace, the method comprising: generating aninitial virtual workspace viewable by a user on a graphical userinterface, the initial virtual workspace comprising a plurality ofworkspace modules comprising data contained in a plurality of dataobjects, the initial virtual workspace associated with i) a first viewof the plurality of workspace modules and ii) a first timestamp;identifying an interaction by the user with at least some of the datacontained in a particular data object of the plurality of data objects;providing, through the initial virtual workspace, at least onesuggestion comprising a description of data contained in the pluralityof data objects that is semantically related to the data interacted withby the user; modifying, based on the suggestion, the initial virtualworkspace to include an additional workspace module comprising datacontained in the plurality of data objects, the modified virtualworkspace associated with i) a second view of the plurality of workspacemodules and the additional workspace module and ii) a second timestamp;filtering the first and second views based on i) a user-selectedtimestamp of the first and the second timestamps and ii) user-selecteddata of the data contained in the plurality of data objects of theinitial workspace and the modified virtual workspace; based on thefiltering, retrieving at least one of the first and second views thatcorresponds to both the user-selected timestamp and the user-selecteddata to provide a retrieved view of the virtual workspace; and preparingthe retrieved view of the virtual workspace for display.
 2. The methodof claim 1, further comprising receiving the suggestion from a contentprovider communicably coupled to the initial virtual workspace.
 3. Themethod of claim 1, wherein identifying an interaction by the user withat least some of the data contained in the plurality of data objectscomprises at least one of: identifying a workspace module added to thevirtual initial workspace by the user; identifying a selection of aworkspace module in the plurality of workspace modules; identifying aselection of the data contained in the particular data object; oridentifying a metric of the particular data object among a plurality ofmetrics.
 4. The method of claim 1, further comprising: identifyingmetadata associated with the data interacted with by the user; andidentifying the data contained in the plurality of data objects that issemantically related to the data interacted with by the user based onthe identified metadata.
 5. The method of claim 4, further comprising:generating a plurality of suggestions, each suggestion comprising adescription of data contained in the plurality of data objects that issemantically related to the data interacted with by the user; andranking the plurality of identified suggestions based on a calculatedrelevancy of each of the plurality of identified suggestions.
 6. Themethod of claim 5, wherein ranking the plurality of identifiedsuggestions based on a calculated relevancy of each of the plurality ofidentified suggestions comprises: determining, for each data object, anoccurrence frequency of at least one keyword from the metadataassociated with the data interacted with by the user in the datacontained in the plurality of data objects; and determining an order ofthe data objects based on the determined occurrence frequencies, whereina data object having the highest occurrence frequency is at a top of theorder and a data object having a lowest occurrence frequency is at abottom of the order.
 7. The method of claim 1, wherein the plurality ofdata objects comprise a first data object and a second data object, themethod further comprising: identifying at least one metric of the firstdata object; matching the identified metric to a metric of the seconddata object; and generating the suggestion comprising a description ofdata contained in the second data object.
 8. The method of claim 1,wherein the plurality of data objects comprise a plurality of datacubes, and wherein identifying an interaction by the user with at leastsome of the data contained in a particular data object of the pluralityof data objects comprises identifying an interaction by the user with atleast some of the data contained in a particular data cube of theplurality of data cubes.
 9. The method of claim 8, further comprising:identifying at least one metric of the particular data cube interactedwith by the user; searching the plurality of data cubes for another datacube having the identified metric of the particular data cube interactedwith by the user; and generating the suggestion comprising a descriptionof data contained in the other data cube.
 10. The method of claim 9,further comprising: querying the other data cube for at least one viewof data contained in the other data cube that matches a view of datacontained in at least one workspace module in the initial virtualworkspace viewable in the graphical user interface; and generating thesuggestion comprising a description of the view of data contained in theother data cube.
 11. The method of claim 10, further comprising:searching the plurality of data cubes for at least one report utilizingthe view of data contained in the other data cube that matches a view ofdata contained in at least one workspace module; and generating thesuggestion comprising a description of the report utilizing the view ofdata contained in the other data cube.
 12. An apparatus comprisinginstructions embodied on a tangible, non-transitory computer-readablemedia, the instructions operable when executed to cause a computingsystem to perform operations comprising: generating an initial virtualworkspace viewable by a user on a graphical user interface, the initialvirtual workspace comprising a plurality of workspace modules comprisingdata contained in a plurality of data objects, the initial virtualworkspace associated with i) a first view of the plurality of workspacemodules and ii) a first timestamp; identifying an interaction by theuser with at least some of the data contained in a particular dataobject of the plurality of data objects; providing, through the initialvirtual workspace, at least one suggestion comprising a description ofdata contained in the plurality of data objects that is semanticallyrelated to the data interacted with by the user; modifying, based on thesuggestion, the initial virtual workspace to include an additionalworkspace module comprising data contained in the plurality of dataobjects, the modified virtual workspace associated with i) a second viewof the plurality of workspace modules and the additional workspacemodule and ii) a second timestamp; filtering the first and second viewsbased on i) a user-selected timestamp of the first and the secondtimestamps and ii) user-selected data of the data contained in theplurality of data objects of the initial workspace and the modifiedvirtual workspace; based on the filtering, retrieving at least one ofthe first and second views that corresponds to both the user-selectedtimestamp and the user-selected data to provide a retrieved view of thevirtual workspace; and preparing the retrieved view of the virtualworkspace for display.
 13. The apparatus of claim 12, wherein theoperations further comprise receiving the suggestion from a contentprovider communicably coupled to the initial virtual workspace.
 14. Theapparatus of claim 12, wherein identifying an interaction by the userwith at least some of the data contained in the plurality of dataobjects comprises at least one of: identifying a workspace module addedto the initial virtual workspace by the user; identifying a selection ofa workspace module in the plurality of workspace modules; identifying aselection of the data contained in the particular data object; oridentifying a metric of the particular data object among a plurality ofmetrics.
 15. The apparatus of claim 12, wherein the operations furthercomprise: identifying metadata associated with the data interacted withby the user; and identifying the data contained in the plurality of dataobjects that is semantically related to the data interacted with by theuser based on the identified metadata.
 16. The apparatus of claim 15,wherein the operations further comprise: generating a plurality ofsuggestions, each suggestion comprising a description of data containedin the plurality of data objects that is semantically related to thedata interacted with by the user; and ranking the plurality ofidentified suggestions based on a calculated relevancy of each of theplurality of identified suggestions.
 17. The apparatus of claim 16,wherein ranking the plurality of identified suggestions based on acalculated relevancy of each of the plurality of identified suggestionscomprises: determining, for each data object, an occurrence frequency ofat least one keyword from the metadata associated with the datainteracted with by the user in the data contained in the plurality ofdata objects; and determining an order of the data objects based on thedetermined occurrence frequencies, wherein a data object having thehighest occurrence frequency is at a top of the order and a data objecthaving a lowest occurrence frequency is at a bottom of the order. 18.The apparatus of claim 12, wherein the plurality of data objectscomprise a first data object and a second data object, and wherein theoperations further comprise: identifying at least one metric of thefirst data object; matching the identified metric to a metric of thesecond data object; and generating the suggestion comprising adescription of data contained in the second data object.
 19. Theapparatus of claim 12, wherein the plurality of data objects comprise aplurality of data cubes, and wherein identifying an interaction by theuser with at least some of the data contained in a particular dataobject of the plurality of data objects comprises identifying aninteraction by the user with at least some of the data contained in aparticular data cube of the plurality of data cubes.
 20. The apparatusof claim 19, wherein the operations further comprise: identifying atleast one metric of the particular data cube interacted with by theuser; searching the plurality of data cubes for another data cube havingthe identified metric of the particular data cube interacted with by theuser; and generating the suggestion comprising a description of datacontained in the other data cube.
 21. The apparatus of claim 20, whereinthe operations further comprise: querying the other data cube for atleast one view of data contained in the other data cube that matches aview of data contained in at least one workspace module in the initialvirtual workspace viewable in the graphical user interface; andgenerating the suggestion comprising a description of the view of datacontained in the other data cube.
 22. The apparatus of claim 21, whereinthe operations further comprise: searching the plurality of data cubesfor at least one report utilizing the view of data contained in theother data cube that matches a view of data contained in at least oneworkspace module; and generating the suggestion comprising a descriptionof the report utilizing the view of data contained in the other datacube.
 23. A computing system, comprising one or more memory modules; oneor more processors; a graphical user interface; and a contextualservices module stored on one or more of the memory modules, thecontextual services module operable when executed with the one or moreprocessors to perform operations comprising: generating an initialvirtual workspace viewable on a graphical user interface, the initialvirtual workspace comprising a plurality of workspace modules comprisingdata contained in a plurality of data objects, the initial virtualworkspace associated with i) a first view of the plurality of workspacemodules and ii) a first timestamp; identifying an interaction by a userof the virtual workspace with at least some of the data contained in aparticular data object of the plurality of data objects; providing,through the initial virtual workspace, at least one suggestioncomprising a description of data contained in the plurality of dataobjects that is semantically related to the data interacted with by theuser; modifying, based on the suggestion, the initial virtual workspaceto include an additional workspace module comprising data contained inthe plurality of data objects, the modified virtual workspace associatedwith i) a second view of the plurality of workspace modules and theadditional workspace module and ii) a second timestamp; filtering thefirst and second views based on i) a user-selected timestamp of thefirst and the second timestamps and ii) user-selected data of the datacontained in the plurality of data objects of the initial workspace andthe modified virtual workspace; and based on the filtering, retrievingat least one of the first and second views that corresponds to both theuser-selected timestamp and the user-selected data to provide aretrieved view of the virtual workspace; and preparing the retrievedview of the virtual workspace for display.
 24. The system of claim 23,wherein the operations further comprise receiving the suggestion from acontent provider communicably coupled to the initial virtual workspace.25. The system of claim 23, wherein identifying an interaction by theuser with at least some of the data contained in the plurality of dataobjects comprises at least one of: identifying a workspace module addedto the initial virtual workspace by the user; identifying a selection ofa workspace module in the plurality of workspace modules; identifying aselection of the data contained in the particular data object; oridentifying a metric of the particular data object among a plurality ofmetrics.
 26. The system of claim 23, wherein the operations furthercomprise: identifying metadata associated with the data interacted withby the user; and identifying the data contained in the plurality of dataobjects that is semantically related to the data interacted with by theuser based on the identified metadata.
 27. The system of claim 26,wherein the operations further comprise: generating a plurality ofsuggestions, each suggestion comprising a description of data containedin the plurality of data objects that is semantically related to thedata interacted with by the user; and ranking the plurality ofidentified suggestions based on a calculated relevancy of each of theplurality of identified suggestions.
 28. The system of claim 27, whereinranking the plurality of identified suggestions based on a calculatedrelevancy of each of the plurality of identified suggestions comprises:determining, for each data object, an occurrence frequency of at leastone keyword from the metadata associated with the data interacted withby the user in the data contained in the plurality of data objects; anddetermining an order of the data objects based on the determinedoccurrence frequencies, wherein a data object having the highestoccurrence frequency is at a top of the order and a data object having alowest occurrence frequency is at a bottom of the order.
 29. The systemof claim 23, wherein the plurality of data objects comprise a first dataobject and a second data object, and wherein the operations furthercomprise: identifying at least one metric of the first data object;matching the identified metric to a metric of the second data object;and generating the suggestion comprising a description of data containedin the second data object.
 30. The system of claim 23, wherein theplurality of data objects comprise a plurality of data cubes, andwherein identifying an interaction by the user with at least some of thedata contained in a particular data object of the plurality of dataobjects comprises identifying an interaction by the user with at leastsome of the data contained in a particular data cube of the plurality ofdata cubes.
 31. The system of claim 30, wherein the operations furthercomprise: identifying at least one metric of the particular data cubeinteracted with by the user; searching the plurality of data cubes foranother data cube having the identified metric of the particular datacube interacted with by the user; and generating the suggestioncomprising a description of data contained in the other data cube. 32.The system of claim 31, wherein the operations further comprise:querying the other data cube for at least one view of data contained inthe other data cube that matches a view of data contained in at leastone workspace module in the initial virtual workspace viewable in thegraphical user interface; and generating the suggestion comprising adescription of the view of data contained in the other data cube. 33.The system of claim 32, wherein the operations further comprise:searching the plurality of data cubes for at least one report utilizingthe view of data contained in the other data cube that matches a view ofdata contained in at least one workspace module; and generating thesuggestion comprising a description of the report utilizing the view ofdata contained in the other data cube.